Parametric EIT for monitoring cardiac stroke volume

Sensitivity volume as figure-of-merit for maximizing data importance in electrical impedance tomography

Objective.Electrical impedance tomography (EIT) is a noninvasive imaging method whereby electrical measurements on the periphery of a heterogeneous conductor are inverted to map its internal conductivity. The EIT method proposed here aims to improve computational speed and noise tolerance by introducing sensitivity volume as a figure-of-merit for comparing EIT measurement protocols.Approach.Each measurement is shown to correspond to a sensitivity vector in model space, such that the set of measurements, in turn, corresponds to a set of vectors that subtend a sensitivity volume in model space. A maximal sensitivity volume identifies the measurement protocol with the greatest sensitivity and greatest mutual orthogonality. A distinguishability criterion is generalized to quantify the increased noise tolerance of high sensitivity measurements.Main result.The sensitivity volume method allows the model space dimension to be minimized to match that of the data space, and the data importance to be increased within an expanded space of measurements defined by an increased number of contacts.Significance.The reduction in model space dimension is shown to increasecomputational efficiency, accelerating tomographic inversion by several orders of magnitude, while the enhanced sensitivitytolerates higher noiselevels up to several orders of magnitude larger than standard methods.

Unsupervised sequence-to-sequence learning for automatic signal quality assessment in multi-channel electrical impedance-based hemodynamic monitoring

BACKGROUND AND OBJECTIVE

This study proposes an unsupervised sequence-to-sequence learning approach that automatically assesses the motion-induced reliability degradation of the cardiac volume signal (CVS) in multi-channel electrical impedance-based hemodynamic monitoring. The proposed method attempts to tackle shortcomings in existing learning-based assessment approaches, such as the requirement of manual annotation for motion influence and the lack of explicit mechanisms for realizing motion-induced abnormalities under contextual variations in CVS over time.

METHOD

By utilizing long-short term memory and variational auto-encoder structures, an encoder-decoder model is trained not only to self-reproduce an input sequence of the CVS but also to extrapolate the future in a parallel fashion. By doing so, the model can capture contextual knowledge lying in a temporal CVS sequence while being regularized to explore a general relationship over the entire time-series. A motion-influenced CVS of low-quality is detected, based on the residual between the input sequence and its neural representation with a cut-off value determined from the two-sigma rule of thumb over the training set.

RESULT

Our experimental observations validated two claims: (i) in the learning environment of label-absence, assessment performance is achievable at a competitive level to the supervised setting, and (ii) the contextual information across a time series of CVS is advantageous for effectively realizing motion-induced unrealistic distortions in signal amplitude and morphology. We also investigated the capability as a pseudo-labeling tool to minimize human-craft annotation by preemptively providing strong candidates for motion-induced anomalies. Empirical evidence has shown that machine-guided annotation can reduce inevitable human-errors during manual assessment while minimizing cumbersome and time-consuming processes.

CONCLUSION

The proposed method has a particular significance in the industrial field, where it is unavoidable to gather and utilize a large amount of CVS data to achieve high accuracy and robustness in real-world applications.

A cascaded convolutional neural networks for stroke detection imaging

In recent years, electrical impedance tomography has widely been used in stroke detection. To improve the prediction accuracy and anti-noise ability of the system, the inverse problem of electrical impedance tomography needs to be solved, for which cascade convolutional neural networks are used. The proposed network is divided into two parts so that the advantages can be compounded when parts of a network are cascaded together. To get high-resolution imaging, an optimized network based on encoding and decoding is designed in the first part. The second part is composed of a residual module, which is used to extract the characteristics of voltage information and ensure that no information is lost. The anti-noise performance of the network is better than other networks. In physical experiments, it is also proved that the algorithm can roughly restore the location of the object in the field.

Machine learning-based signal quality assessment for cardiac volume monitoring in electrical impedance tomography

Owing to recent advances in thoracic electrical impedance tomography (EIT), a patient’s hemodynamic function can be noninvasively and continuously estimated in real-time by surveilling a cardiac volume signal (CVS) associated with stroke volume and cardiac output. In clinical applications, however, a CVS is often of low quality, mainly because of the patient’s deliberate movements or inevitable motions during clinical interventions. This study aims to develop a signal quality indexing method that assesses the influence of motion artifacts on transient CVSs. The assessment is performed on each cardiac cycle to take advantage of the periodicity and regularity in cardiac volume changes. Time intervals are identified using the synchronized electrocardiography system. We apply divergent machine-learning methods, which can be sorted into discriminative-model and manifold-learning approaches. The use of machine-learning could be suitable for our real-time monitoring application that requires fast inference and automation as well as high accuracy. In the clinical environment, the proposed method can be utilized to provide immediate warnings so that clinicians can minimize confusion regarding patients’ conditions, reduce clinical resource utilization, and improve the confidence level of the monitoring system. Numerous experiments using actual EIT data validate the capability of CVSs degraded by motion artifacts to be accurately and automatically assessed in real-time by machine learning. The best model achieved an accuracy of 0.95, positive and negative predictive values of 0.96 and 0.86, sensitivity of 0.98, specificity of 0.77, and AUC of 0.96.

Case-Specific Focal Sensor Design for Cardiac Electrical Impedance Tomography

Electrical impedance tomography (EIT) is a non-invasive detection technology that uses the electrical response value at the boundary of an observation field to image the conductivity changes in an area. When EIT is applied to the thoracic cavity of the human body, the conductivity change caused by the heartbeat will be concentrated in a sub-region of the thoracic cavity, that is, the heart region. In order to improve the spatial resolution of the target region, two sensor optimization methods based on conformal mapping theory were proposed in this study. The effectiveness of the proposed method was verified by simulation and phantom experiment. The qualitative analysis and quantitative index evaluation of the reconstructed image showed that the optimized model could achieve higher imaging accuracy of the heart region compared with the standard sensor. The reconstruction results could effectively reflect the periodic diastolic and systolic movements of the heart and had a better ability to recognize the position of the heart in the thoracic cavity.

Multi-channel Trans-impedance Leadforming for Cardiopulmonary Monitoring: Algorithm Development and Feasibility Assessment using In Vivo Animal Data

OBJECTIVE

The objectives of this study were to (1) develop a multi-channel trans-impedance leadforming method for beat-to-beat stroke volume (SV) and breath-by-breath tidal volume (TV) measurements and (2) assess its feasibility on an existing in vivo animal dataset.

METHODS

A deterministic leadforming algorithm was developed to extract a cardiac volume signal (CVS) and a respiratory volume signal (RVS) from 208-channel trans-impedance data acquired every 20 ms by an electrical impedance tomography (EIT) device. SV_EIT and TV_EIT values were computed as a valley-to-peak value in the CVS and RVS, respectively. The method was applied to the existing dataset from five mechanically-ventilated pigs undergoing ten mini-fluid challenges. An invasive hemodynamic monitor was used in the arterial pressure-based cardiac output (APCO) mode to simultaneously measure SV_APCO values while a mechanical ventilator provided TV_Vent values.

RESULTS

The leadforming method could reliably extract the CVS and RVS from the 208-channel trans-impedance data measured with the EIT device, from which SV_EIT and TV_EIT were computed. The SV_EIT and TV_EIT values were comparable to those from the invasive hemodynamic monitor and mechanical ventilator. Using the data from 5 pigs and a simple calibration method to remove bias, the error in SV<sub>EIT<sub> and TV<sub>EIT<sub> was 9.5% and 5.4%, respectively.

CONCLUSION

We developed a new leadforming method for the EIT device to robustly extract both SV and TV values in a deterministic fashion. Future animal and clinical studies are needed to validate this leadforming method in various subject populations.

SIGNIFICANCE

The leadforming method could be an integral component for a new cardiopulmonary monitor in the future to simultaneously measure SV and TV noninvasively, which would be beneficial to patients.

Electrical impedance tomography for non-invasive identification of fatty liver infiltrate in overweight individuals

Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes of cardiometabolic diseases in overweight individuals. While liver biopsy is the current gold standard to diagnose NAFLD and magnetic resonance imaging (MRI) is a non-invasive alternative still under clinical trials, the former is invasive and the latter costly. We demonstrate electrical impedance tomography (EIT) as a portable method for detecting fatty infiltrate. We enrolled 19 overweight subjects to undergo liver MRI scans, followed by EIT measurements. The MRI images provided the a priori knowledge of the liver boundary conditions for EIT reconstruction, and the multi-echo MRI data quantified liver proton-density fat fraction (PDFF%) to validate fat infiltrate. Using the EIT electrode belts, we circumferentially injected pairwise current to the upper abdomen, followed by acquiring the resulting surface-voltage to reconstruct the liver conductivity. Pearson's correlation analyses compared EIT conductivity or MRI PDFF with body mass index, age, waist circumference, height, and weight variables. We reveal that the correlation between liver EIT conductivity or MRI PDFF with demographics is statistically insignificant, whereas liver EIT conductivity is inversely correlated with MRI PDFF (R = -0.69, p = 0.003, n = 16). As a pilot study, EIT conductivity provides a portable method for operator-independent and cost-effective detection of hepatic steatosis.

Diagnosing and monitoring pleural effusion using parametric electrical impedance tomography - a computational 3D model and preliminary experimental results

PURPOSE

Diagnosing and monitoring pleural effusion (PE) is challenging due unsuitability of existing modalities. In the present study, a novel parametric electrical impedance tomography (pEIT) technique, tailored to a clinically feasible system to diagnose PE is presented.

METHODS

An electrical impedance tomography (EIT) numeric solver was applied to a 3D realistic normal model and five PE models to simulate sets of surface measurements. Simulations were triggered by a series of eight independent projections using five electrodes positioned around the thorax. The relative changes in the potential between the PE models and the normal model were assessed and the error in the estimated PE volume was examined at varying signal to noise ratio (SNR) levels. For experimental feasibility, measurements were performed in four healthy subjects and were correlated with the potentials that were calculated from the normal model.

RESULTS

Relative potential changes were notable (reached until ~55%) and increased with the increasing PE volumes. Maximal error of ± 20 [mL] was obtained for SNR levels >50 [dB]. The feasibility real measurements in healthy subjects showed a strong linear correlation (R² > 0.85) and a successful diagnosis for all subjects.

CONCLUSION

The proposed technique can estimate PE volumes from a redundant set of measurements in a realistic 3D human model and may be utilized for monitoring PE patients.

Noninvasive, simultaneous, and continuous measurements of stroke volume and tidal volume using EIT: feasibility study of animal experiments

Currently, there is no noninvasive method available for simultaneous measurements of tidal volume and stroke volume. Electrical impedance tomography (EIT) has been used for regional lung ventilation imaging. Cardiac EIT imaging, however, has not been successful due to the technical difficulty in extracting weak cardiogenic components. Instead of regional imaging, in this paper, we use the EIT technique to simultaneously measure two global variables of tidal volume and stroke volume. Time-varying patterns of boundary voltage data originating from lung ventilation and cardiac blood flow were extracted from measured boundary voltage data using the principal component analysis (PCA) and independent component analysis (ICA). The source consistency theory was adopted to separately synthesize time-series of boundary voltage data associated with lung ventilation and cardiac blood flow. The respiratory volume signal (RVS) and cardiac volume signal (CVS) were extracted from reconstructed time-difference EIT images of lung ventilation and cardiac blood flow, respectively. After calibrating the volume signals using the mechanical ventilator and the invasive transpulmonary thermodilution (TPTD) method, tidal volume and stroke volume were computed as valley-to-peak values of the RVS and CVS, respectively. The difference in the tidal volume data between EIT and mechanical ventilator was within ± 20 ml from six pigs. The difference in the stroke volume data between EIT and TPTD was within ± 4.7 ml from the same animals. The results show the feasibility of the proposed method as a new noninvasive cardiopulmonary monitoring tool for simultaneous continuous measurements of stroke volume and tidal volume that are two most important vital signs.

Metabolic Profiling in Bipolar Disorder Patients During Depressive Episodes

Bipolar disorder (BD) is a common and debilitating mental disorder. Bipolar depression is the main episode of BD. Furthermore, there are no objective biomarkers available for diagnosing the disorder. In this research, a Nuclear Magnetic Resonance (NMR) spectroscopy based on a metabonomics technique was used to analyze serum samples from 37 patients with bipolar depression and 48 healthy control participants to determine potential biomarkers for bipolar depression. In total, seven different metabolites were identified that could effectively distinguish patients from healthy controls. The metabolites indicated that disturbances of amino acid and energy metabolisms might be involved in the pathogenesis of BD. Finally, a panel consisting of four potential biomarkers (lactate, trimethylamine oxide, N-acetyl glycoprotein, and α-glucose) was identified, which showed a higher combined diagnostic ability with an area under the curve of 0.893. Our findings may contribute to the development of an objective method for diagnosing bipolar depression.

Advances of Metabolomics in Fungal Pathogen-Plant Interactions

Plant disease caused by fungus is one of the major threats to global food security, and understanding fungus-plant interactions is important for plant disease control. Research devoted to revealing the mechanisms of fungal pathogen-plant interactions has been conducted using genomics, transcriptomics, proteomics, and metabolomics. Metabolomics research based on mass spectrometric techniques is an important part of systems biology. In the past decade, the emerging field of metabolomics in plant pathogenic fungi has received wide attention. It not only provides a qualitative and quantitative approach for determining the pathogenesis of pathogenic fungi but also helps to elucidate the defense mechanisms of their host plants. This review focuses on the methods and progress of metabolomics research in fungal pathogen-plant interactions. In addition, the prospects and challenges of metabolomics research in plant pathogenic fungi and their hosts are addressed.

Non-Invasive Electrical Impedance Tomography for Multi-Scale Detection of Liver Fat Content

Introduction: Obesity is associated with an increased risk of nonalcoholic fatty liver disease (NAFLD). While Magnetic Resonance Imaging (MRI) is a non-invasive gold standard to detect fatty liver, we demonstrate a low-cost and portable electrical impedance tomography (EIT) approach with circumferential abdominal electrodes for liver conductivity measurements.

Methods and Results: A finite element model (FEM) was established to simulate decremental liver conductivity in response to incremental liver lipid content. To validate the FEM simulation, we performed EIT imaging on an ex vivo porcine liver in a non-conductive tank with 32 circumferentially-embedded electrodes, demonstrating a high-resolution output given a priori information on location and geometry. To further examine EIT capacity in fatty liver detection, we performed EIT measurements in age- and gender-matched New Zealand White rabbits (3 on normal, 3 on high-fat diets). Liver conductivity values were significantly distinct following the high-fat diet (p = 0.003 vs. normal diet, n=3), accompanied by histopathological evidence of hepatic fat accumulation. We further assessed EIT imaging in human subjects with MRI quantification for fat volume fraction based on Dixon procedures, demonstrating average liver conductivity of 0.331 S/m for subjects with low Body-Mass Index (BMI < 25 kg/m²) and 0.286 S/m for high BMI (> 25 kg/m²).

Conclusion: We provide both the theoretical and experimental framework for a multi-scale EIT strategy to detect liver lipid content. Our preliminary studies pave the way to enhance the spatial resolution of EIT as a marker for fatty liver disease and metabolic syndrome.

Parametric electrical impedance tomography for measuring bone mineral density in the pelvis using a computational model

Osteoporosis is defined as bone microstructure deterioration resulting a decrease of bone's strength. Measured bone mineral density (BMD) constitutes the main tool for Osteoporosis diagnosis, management, and defines patient's fracture risk. In the present study, parametric electrical impedance tomography (pEIT) method was examined for monitoring BMD, using a computerized simulation model and preliminary real measurements. A numerical solver was developed to simulate surface potentials measured over a 3D computerized pelvis model. Varying cortical and cancellous BMD were simulated by changing bone conductivity and permittivity. Up to 35% and 16% change was found in the real and imaginary modules of the calculated potential, respectively, while BMD changes from 100% (normal) to 60% (Osteoporosis). Negligible BMD relative error was obtained with SNR>60 [dB]. Position changes errors indicate that for long term monitoring, measurement should be taken at the same geometrical configuration with great accuracy. The numerical simulations were compared to actual measurements that were acquired from a healthy male subject using a five electrodes belt bioimpedance device. The results suggest that pEIT may provide an inexpensive easy to use tool for frequent monitoring BMD in small clinics during pharmacological treatment, as a complementary method to DEXA test.

Influence of heart motion on cardiac output estimation by means of electrical impedance tomography: a case study

Electrical impedance tomography (EIT) is a non-invasive imaging technique that can measure cardiac-related intra-thoracic impedance changes. EIT-based cardiac output estimation relies on the assumption that the amplitude of the impedance change in the ventricular region is representative of stroke volume (SV). However, other factors such as heart motion can significantly affect this ventricular impedance change. In the present case study, a magnetic resonance imaging-based dynamic bio-impedance model fitting the morphology of a single male subject was built. Simulations were performed to evaluate the contribution of heart motion and its influence on EIT-based SV estimation. Myocardial deformation was found to be the main contributor to the ventricular impedance change (56%). However, motion-induced impedance changes showed a strong correlation (r = 0.978) with left ventricular volume. We explained this by the quasi-incompressibility of blood and myocardium. As a result, EIT achieved excellent accuracy in estimating a wide range of simulated SV values (error distribution of 0.57 ± 2.19 ml (1.02 ± 2.62%) and correlation of r = 0.996 after a two-point calibration was applied to convert impedance values to millilitres). As the model was based on one single subject, the strong correlation found between motion-induced changes and ventricular volume remains to be verified in larger datasets.

Assessment of cardiac stroke volume in patients with implanted cardiac pacemaker using parametric electrical impedance tomography: a theoretical 2D study

The present theoretical study examines the ability to estimate cardiac stroke volume (CSV) in patients with implanted cardiac pacemaker using parametric electrical impedance tomography (pEIT) in a 2D computerized model of the thorax. CSV is a direct indicator of the cardiac pumping efficiency. The commonly used methods for measuring CSV require the invasive procedure of right heart catheterization or use expensive imaging techniques (i.e., MRI). Hence, experience with these techniques for diagnosis and monitoring has been limited to hospitalized patients. In the present study, pEIT scheme was applied in a computerized 2D model of the human thorax with implanted cardiac device to determine the left ventricular (LV) volume at different cardiac cycle phases. The LV was simulated as a prolate ellipse with its axes' lengths as the reconstruction parameters while all other geometries and conductivity values remained constant. An optimization was carried out in order to ensure that the ellipse is the appropriate model for the LV at each cardiac cycle phase. LV volumes calculated by both the pEIT algorithm and the ellipsoid model are consistent. A high correlation (ρ = 0.99) between the true and reconstructed volumes was found. The SV calculation error was ∼1%. The results suggest that the LV volume can be estimated using the pEIT method in a 2D computerized model, and that the method has the potential to be used for monitoring patients with implanted cardiac pacemaker.

Evaluation of different stimulation and measurement patterns based on internal electrode: application in cardiac impedance tomography

The conductivity distribution around the thorax is altered during the cardiac cycle due to the blood perfusion, heart contraction and lung inflation. Previous studies showed that these bio-impedance changes are appropriate for non-invasive cardiac function imaging using Electrical Impedance Tomography (EIT) techniques. However, the spatial resolution is presently low. One of the main obstacles in cardiac imaging at the heart location is the large impedance variation of the lungs by respiration and muscles on the dorsal and posterior side of the body. In critical care units there is a potential to insert an internal electrode inside the esophagus directly behind the heart in the same plane of the external electrodes. The aim of the present study is to evaluate different current stimulation and measurement patterns with both external and internal electrodes. Analysis is performed with planar arrangement of 16 electrodes for a simulated 3D cylindrical tank and pig thorax model. In our study we evaluated current injection patterns consisting of adjacent, diagonal, trigonometric, and radial to the internal electrode. The performance of these arrangements was assessed using quantitative methods based on distinguishability, sensitivity and GREIT (Graz consensus Reconstruction algorithm for Electrical Impedance Tomography). Our evaluation shows that an internal electrode configuration based on the trigonometric injection patterns has better performance and improves pixel intensity of the small conductivity changes related to heart near 1.7 times in reconstructed images and also shows more stability with different levels of added noise. For the internal electrode, when we combined radial or adjacent injection with trigonometric injection pattern, we found an improvement in amplitude response. However, the combination of diagonal with trigonometric injection pattern deteriorated the shape deformation (correlation coefficient r=0.344) more than combination of radial and trigonometric injection (correlation coefficient r=0.836) for the perturbations in the area close to the center of the cylinder. We also find that trigonometric stimulation pattern performance is degraded in a realistic thorax model with anatomical asymmetry. For that reason we recommend using internal electrodes only for voltage measurements and as a reference electrode during trigonometric stimulation patterns in practical measurements.

Monitoring retroperitoneal bleeding model of piglets by electrical impedance tomography

To investigate continuous monitoring capacity of electrical impedance tomography (EIT) for retroperitoneal bleeding, studies were carried out on six anesthetized piglet's bleeding model produced by injecting anticoagulated blood into renal region. For each subject, total blood of about 200 ml was injected within time periods ranging from tens of minutes to several hours. The simulated bleeding processes were detected and monitored by EIT system with sixteen electrodes at a rate of one image per second. EIT images were reconstructed by dynamic back-projection algorithm. The results showed that impedance changes caused by bleeding could be revealed by EIT images.

Image monitoring for an intraperitoneal bleeding model of pigs using electrical impedance tomography

Current medical imaging techniques are not effective for timely detection of internal hemorrhage when the bleeding is slow and in small quantities. In this study, electrical impedance tomography (EIT) was applied to monitor the intraperitoneal bleeding of an animal model. Five healthy pigs three months old were used. The process of intraperitoneal bleeding was simulated with the injection of anticoagulated blood which was controlled by an electronic syringe pump. The injected rate was no more than 100 ml h(-1) and the total injection volumes ranged from 300 ml to 500 ml. Sixteen electrodes were attached to the abdomen and used for electrical current excitation and surface voltage measurement. Dynamic changes in impedance distribution within the abdomen were calculated by the back-projection algorithm and a series of EIT images were displayed in a unified range. The monitoring was performed with EIT at a rate of one frame per second and continued for at least 4 h. Intraperitoneal blood volume changes could be identified by inspection of consecutive EIT images during the progression of blood injection. 30 ml of blood in the peritoneum could be detected. EIT was shown to be a promising technique for continuous monitoring of intraperitoneal bleeding over periods of time.